Update README

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- text-generation
+- text2text-generation
+pipeline_tag: text2text-generation
+widget:
+- text: "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons."
+  example_title: "Example1"
+- text: "Summarize: Jorge Alfaro drove in two runs, Aaron Nola pitched seven innings of two-hit ball and the Philadelphia Phillies beat the Los Angeles Dodgers 2-1 Thursday, spoiling Clayton Kershaw's first start in almost a month. Hitting out of the No. 8 spot in the ..."
+  example_title: "Example2"
+---
+
+# MTL-summarization
+The MTL-summarization model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://github.com/RUCAIBox/MVP/blob/main/paper.pdf) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
+
+The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP).
+
+## Model Description
+MTL-summarization is supervised pre-trained using a mixture of labeled summarization datasets. It is a variant (Single) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a standard Transformer encoder-decoder architecture.
+
+MTL-summarization is specially designed for summarization tasks, such as new summarization (CNN/DailyMail, XSum) and dialog summarization (SAMSum).
+
+## Example
+```python
+>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
+
+>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
+>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-summarization")
+
+>>> inputs = tokenizer(
+...     "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.",
+...     return_tensors="pt",
+... )
+>>> generated_ids = model.generate(**inputs)
+>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+["Don't do it if these are your reasons"]
+```
+
+## Citation